--- title: "Understanding AI Security: Risks, Challenges, and Defenses" date: 2026-06-09T15:36:31Z modified: 2026-06-09T15:59:25Z permalink: "https://www.venn.com/learn/ai-security/" type: knowledge status: publish excerpt: "" wpid: 6116 featured_image: "https://www.venn.com/wp-content/uploads/2026/06/BYOAI-1.png" parent: "" ancestors: [] children: - 6004 - 6002 - 5946 - 5943 - 5917 --- ## What Is AI Security? AI security is the practice of protecting AI systems from threats. It defends data, models, and infrastructure throughout their lifecycle to prevent tampering, prompt injection, and data leakage. Effective security requires validating inputs, managing API access, and establishing robust AI guardrails. As organizations increasingly integrate AI into critical business processes, the attack surface expands. AI systems, especially large language models (LLMs), can be targeted in ways that differ from conventional IT systems, such as prompt injection, data poisoning, and model theft. Protecting AI goes beyond technical measures; it also includes monitoring model behavior, ensuring compliance with regulations, and managing access to sensitive data. A comprehensive AI security strategy considers the full lifecycle of AI systems, from development and deployment to ongoing operation and eventual decommissioning. Free eBook: **Secure Remote Access that Doesn’t Drive Users Crazy!** Secure your entire extended workforce without issuing devices or VDI. Keep your organization agile, compliant, and secure. ![](https://www.venn.com/wp-content/uploads/2025/09/How-to-Secure-contractor-access-on-unmanaged-endpoints.png) ## In this article: - [Why Is AI Security Important?](#h-why-is-ai-security-important) - [AI Security vs. AI Safety](#h-ai-security-vs-ai-safety) - [Key AI Security Risks: OWASP Top 10 for LLMs](#h-key-ai-security-risks-owasp-top-10-for-llms) - [Security Risks in Other Types of AI Systems](#h-security-risks-in-other-types-of-ai-systems) - [AI Security Use Cases](#h-ai-security-use-cases) - [Challenges in AI Security](#h-challenges-in-ai-security) - [Key Features of AI Security Solutions](#h-key-features-of-ai-security-solutions) - [Key AI Security Best Practices](#h-key-ai-security-best-practices) ## Why Is AI Security Important? AI security matters because failures in AI systems can lead to business, legal, and safety consequences. Unlike traditional software, AI systems can be influenced by data and inputs in unpredictable ways. This makes them a target for new attack methods and increases the potential impact of a breach or misuse. Implementing AI security: - **Protects sensitive data**: AI models often rely on large volumes of proprietary or personal data. Weak controls can expose training data through model outputs or leaks, leading to privacy violations and regulatory penalties. - **Prevents manipulation of model behavior**: Attacks such as prompt injection or adversarial inputs can alter how a model responds. This can result in incorrect decisions, unsafe outputs, or unauthorized actions in downstream systems. - **Reduces risk of data poisoning**: If attackers can influence training data, they can embed hidden behaviors or biases into the model. - **Guards against model theft and abuse**: Trained models are valuable intellectual property. Without proper protections, attackers can extract or replicate models. - **Maintains system reliability and trust**: AI systems are often used in critical workflows. Security issues can reduce accuracy, availability, or consistency. - **Supports regulatory compliance**: Many industries require strict controls over data usage and system behavior. AI security helps meet requirements related to privacy, accountability, and auditability. - **Limits operational and financial impact**: Security incidents involving AI can disrupt services and trigger incident response costs. - **Addresses expanding attack surface**: AI introduces new components such as data pipelines, model APIs, and inference endpoints. ## AI Security vs. AI Safety AI security and AI safety are related but distinct concepts. **AI security** focuses on protecting AI systems from threats such as attacks, misuse, or unauthorized access. It is concerned with technical defenses, monitoring, and controls that prevent malicious actors from compromising AI models, data, or infrastructure. The goal is to safeguard the confidentiality, integrity, and availability of AI-driven processes and assets. **AI safety** focuses on ensuring that AI systems operate in a way that aligns with intended goals and does not cause unintended harm, even in the absence of malicious actors. Safety includes preventing biased outcomes, ensuring robustness to unexpected inputs, and keeping AI actions within ethical and regulatory boundaries. While security protects against deliberate threats, safety addresses accidental or emergent risks from the AI itself. Both are necessary for trustworthy AI, but they address different aspects of risk management. ## Key AI Security Risks: OWASP Top 10 for LLMs The OWASP Top 10 for LLMs highlights common and impactful security risks specific to AI applications. These risks reflect how LLMs are integrated into real systems, where inputs, data flows, and external dependencies create new attack paths: - **Prompt injection (LLM01)**: Malicious inputs can change model behavior, bypass safeguards, or trigger unintended actions such as data access or command execution. - **Insecure output handling (LLM02):** Failing to validate or sanitize model outputs can lead to downstream exploits such as code injection, XSS, or system compromise. - **Training data poisoning (LLM03):** Attackers can manipulate training or fine-tuning data to introduce biases, backdoors, or degraded performance. - **Model denial of service (LLM04)**: Attackers may overwhelm LLM resources with heavy or numerous operations, resulting in DoS. - **Supply chain vulnerabilities (LLM05)**: Risks arise from third-party models, datasets, and tools, including tampered models, poisoned dependencies, or untrusted sources. - **Sensitive information disclosure (LLM06)**: Models may expose personal data, credentials, or proprietary information through outputs. - **Excessive agency (LLM08):** Granting models too many permissions or autonomous actions can result in unintended or harmful operations across connected systems. - **Overreliance (LLM09):** Users or systems may trust model outputs without validation, leading to incorrect decisions, propagation of errors, or unsafe actions based on flawed responses. - **Model theft (LLM10):** Attackers may extract or replicate a model through repeated queries or access to weights, exposing intellectual property and enabling misuse. ## Security Risks in Other Types of AI Systems Not all AI systems are based on LLMs. Many organizations use machine learning models for fraud detection, recommendation engines, computer vision, predictive maintenance, biometric verification, malware detection, autonomous systems, and risk scoring. These systems face different security risks because they often rely on structured data, sensor inputs, images, behavioral patterns, or automated decisions rather than natural language prompts. **Common risks in non-LLM AI systems include:** - **Adversarial inputs**: Attackers can make small, intentional changes to inputs, such as images, audio, sensor readings, or transaction data, to cause a model to misclassify them. For example, an altered image may bypass a computer vision system, or manipulated transaction patterns may evade fraud detection. - **Model evasion**: Attackers may study how a model responds and adjust their behavior to avoid detection. This is common in systems used for spam filtering, malware detection, fraud prevention, and abuse monitoring. - **Model inversion**: Attackers may use model outputs to infer sensitive information about the training data, potentially exposing personal, proprietary, or confidential information. - **Membership inference**: Attackers may determine whether a specific person, record, or data point was included in a model’s training dataset, creating privacy and compliance risks. - **Transfer learning risks**: Models adapted from pretrained or third-party sources may inherit hidden vulnerabilities, biased behaviors, or malicious modifications. - **Output manipulation**: Attackers may exploit weaknesses in how model outputs are interpreted by downstream systems, especially when AI results trigger automated decisions or alerts. - **Model skewing and drift abuse**: Attackers may intentionally influence the data environment around a deployed model so that its performance degrades over time or shifts in a way that benefits the attacker. - **Shadow AI usage**: Employees may use unauthorized AI tools or upload sensitive data to external systems without security, legal, or compliance review. These risks are especially important for AI systems used in high-impact domains such as finance, healthcare, cybersecurity, identity verification, transportation, and industrial operations, where incorrect predictions or manipulated outputs can have serious business, safety, or legal consequences. ## AI Security Use Cases ### Governing AI Use on Unmanaged and BYOD Devices Employees increasingly use AI tools from personal or unmanaged laptops, especially in remote and hybrid environments. This creates risk when sensitive company data can be copied into public AI tools, uploaded through browser sessions, or exposed through local apps and OS-level AI features. AI security helps organizations define which AI tools are approved for work, enforce where those tools can access company data, and block unauthorized AI workflows. This is especially important for organizations that allow contractors, remote employees, or distributed teams to work from their own devices. Security controls should prevent protected data from being copied, uploaded, pasted, or captured by unapproved AI systems while still allowing users to benefit from sanctioned AI tools. ### Preventing Sensitive Data Leakage into AI Tools One of the most common AI security use cases is stopping confidential data from being entered into AI applications. Employees may unintentionally paste customer records, source code, financial data, legal documents, or internal strategy into AI tools to summarize, analyze, or rewrite content. AI security controls can reduce this risk by applying data loss prevention policies across files, browsers, clipboard activity, uploads, downloads, and screen capture. These controls help ensure that sensitive work data remains within approved environments and cannot be moved into personal AI tools or unmanaged applications. ### Securing AI Workflows for Remote Employees and Contractors Remote employees and contractors often need access to company applications, files, and data without receiving a corporate-managed laptop. When these users also rely on AI tools, organizations need a way to secure AI workflows without taking over the entire personal device. AI security can isolate work activity from personal activity, enforce access policies for business applications, and ensure that approved AI tools operate only within governed workspaces. This allows organizations to support flexible work models while maintaining control over how company data is accessed, processed, and shared. **_Related content: read our guide to [secure AI deployment](https://www.venn.com/learn/ai-security/secure-ai-deployment/)_** ### Protecting Regulated and Client Data Industries such as healthcare, financial services, legal services, and professional services handle sensitive data that must be protected under privacy, security, and contractual requirements. AI tools can create compliance risks if regulated information or client data is exposed to systems that are not approved for that use. AI security helps enforce boundaries around regulated data by controlling where it can be accessed, copied, stored, or processed. It also supports auditability by giving organizations visibility into work-related AI usage and policy enforcement without monitoring personal activity outside the work environment. ### Maintaining User Privacy While Protecting Company Data AI security on personal devices must balance corporate control with employee privacy. Organizations need visibility and enforcement over work data, but they should avoid monitoring personal files, browsing, or applications. A strong AI security approach separates work from personal activity. Company data and approved work applications can be governed by policy, while personal use remains outside the managed environment. This model helps improve adoption among employees and contractors because security controls do not require invasive device management. ### Supporting Secure AI Adoption Many organizations want to encourage AI adoption but are concerned about data exposure, compliance violations, and lack of control. Blocking AI entirely can slow productivity, while allowing unrestricted use can increase risk. AI security provides a middle path. It enables approved AI tools and workflows under defined policies, monitors work-related usage, and prevents sensitive data from reaching untrusted systems. This allows organizations to benefit from AI-assisted productivity while maintaining control over data, access, and compliance. ## Challenges in AI Security ### Lack of Transparency A major challenge in AI security is the lack of transparency in how AI models make decisions. Many machine learning models, especially deep learning architectures, operate as “black boxes,” making it difficult to understand their reasoning or detect when they have been compromised. This opacity complicates efforts to audit models, investigate incidents, or ensure compliance with regulations that require explainability. Without transparency, it is difficult to identify whether an AI system is producing biased, manipulated, or unsafe outputs. Attackers can exploit this lack of insight to introduce subtle vulnerabilities or evade detection. ### Rapid Evolution of Threats The threat landscape for AI systems is evolving, with new attack techniques and vulnerabilities emerging as AI adoption grows. Adversaries exploit weaknesses in AI architectures, targeting both models and supporting infrastructure. As organizations deploy new AI capabilities, they must defend against attacks not typically seen in traditional IT environments. This pace challenges existing security tools and practices, which may not address AI-specific risks such as adversarial examples, model inversion, or prompt injection. ### Skills Gap AI security requires a specialized skill set that combines knowledge of machine learning, cybersecurity, and data privacy. There is a shortage of professionals with expertise in both AI development and security, creating a skills gap that hampers risk management. Many organizations struggle to find or train staff who can assess AI-specific threats, implement defenses, and respond to incidents involving AI systems. This gap can lead to misconfigured models, overlooked vulnerabilities, or inadequate response to emerging threats. ### Balancing Innovation vs. Security Organizations face pressure to innovate rapidly with AI to stay competitive, which can lead to shortcuts in security practices. Fast-paced development cycles may prioritize model performance or business outcomes over risk assessments and security testing. As a result, vulnerabilities can be introduced or left unaddressed. Balancing innovation with security requires integrating security considerations into every phase of the AI lifecycle, from data collection and model training to deployment and monitoring. **_Related content: read our guide to [endpoint AI security](https://www.venn.com/learn/ai-security/endpoint-ai-security/)_** ## Key Features of AI Security Solutions ### Centralized AI Visibility and Discovery Organizations often lack a clear inventory of where AI is used, which models are deployed, and how data flows between components. Centralized visibility addresses this by discovering AI assets across environments, including models, APIs, datasets, and third-party integrations. It creates a unified view of the AI attack surface. This visibility enables security teams to track usage, detect shadow AI deployments, and assess risk exposure. It also supports auditing and compliance by maintaining a record of AI systems, their configurations, and access patterns. Related content: read our guide to [AI experimentation](https://www.venn.com/learn/ai-security/ai-experimentation/) ### Real-Time Prompt and Output Protection AI systems that interact with users or external inputs require runtime protection. Real-time controls inspect prompts before they reach the model and validate outputs before they are consumed by downstream systems. This helps prevent prompt injection, data leakage, and unsafe responses. These protections include input filtering, context isolation, and output sanitization. By enforcing checks during inference, organizations can reduce the risk of exploitation without retraining models or disrupting normal usage. ### AI Governance and Policy Enforcement AI governance defines how models should be used, what data they can access, and which actions they are allowed to take. Security solutions enforce these policies across the AI lifecycle, from development to production. Policy enforcement includes access controls, usage restrictions, and audit logging. It ensures that AI systems operate within defined boundaries and supports compliance with internal standards and external regulations. ### Data Security and Data Loss Prevention (DLP for AI) AI models often process sensitive data, making data protection a core requirement. [DLP](https://www.venn.com/learn/dlp/) for AI focuses on preventing unauthorized exposure of sensitive information through prompts, training data, or model outputs. This involves detecting sensitive content, applying masking or redaction, and restricting data flows based on policy. It also includes safeguards against training data leakage and model inversion attacks. Additional controls may include token-level inspection, encryption of data in use, and strict separation between tenants or workloads. **_Learn more in our detailed guide to_** [**_data security_**](https://www.venn.com/learn/data-security/) ### Integration with Security Stack AI security solutions are most effective when integrated with existing security tools such as SIEM, SOAR, identity providers, and endpoint protection platforms. Integration allows AI-related events to be correlated with broader security signals. This approach improves detection and response by providing context across systems. It enables automated workflows, centralized alerting, and consistent enforcement of security policies. ## Key AI Security Best Practices Here are some of the ways that organizations can better secure their AI systems. ### 1. Gain Full Visibility into AI Usage (Shadow AI Control) Organizations often deploy AI faster than they can track it, leading to “[shadow AI](https://www.venn.com/learn/ai-security/shadow-ai/)” across teams. This includes unmanaged APIs, third-party tools, and embedded models in applications. Without visibility, security teams cannot assess risk or enforce controls. Start by discovering all AI assets, including models, datasets, prompts, and integrations. Map how data flows between these components and identify who is using them. Maintain an up-to-date inventory and classify assets by sensitivity and exposure. Continuous monitoring is required, not a one-time scan. New tools and endpoints appear quickly. Automated discovery and integration with asset management systems help ensure that no AI system operates outside governance. ### 2. Enforce Strong Data Governance for AI Data is the foundation of AI systems, and weak governance introduces risk at every stage. This includes training data, fine-tuning datasets, and inputs processed during inference. Sensitive data must be identified, classified, and protected consistently. Apply strict controls on what data can be used for training and what can be sent to models. Use masking, anonymization, or tokenization where possible. Limit retention and ensure that data handling complies with regulations and internal policies. Governance should also address data lineage. Track where data originates, how it is transformed, and where it is used. This improves auditability and helps detect issues such as data poisoning or unintended exposure. ### 3. Implement Identity and Access Control for AI Systems AI systems should follow the same identity principles as other critical infrastructure. Every user, service, and application interacting with AI must be authenticated and authorized. Avoid shared credentials or broad access permissions. Use least-privilege access to restrict who can query models, modify prompts, or access training data. Separate roles for development, deployment, and operations to reduce the risk of misuse or accidental changes. Integrate AI systems with identity providers and enforce multi-factor authentication where appropriate. Monitor access patterns to detect anomalies such as unusual query volumes or access from unexpected locations. ### 4. Monitor AI Activity in Real Time AI systems operate dynamically, making real-time monitoring essential. Logs should capture prompts, outputs, user actions, and system behavior. This data helps detect attacks such as prompt injection, data exfiltration, or abnormal usage. Set up alerting for suspicious patterns, including spikes in usage, repeated failed requests, or attempts to bypass controls. Correlate AI activity with other security signals to gain context during investigations. Monitoring should also include model performance and output quality. Sudden changes may indicate poisoning, drift, or exploitation. Continuous visibility allows faster detection and response to security and operational issues. ### 5. Use AI Security Frameworks and Standards Established frameworks provide structured guidance for managing AI risk. Examples include the [NIST AI Risk Management Framework](https://www.venn.com/learn/ai-security/nist-ai-risk-management-framework/) and the OWASP Top 10 for LLMs. These frameworks help identify threats, define controls, and measure maturity. Adopting a framework promotes consistency across teams and aligns security efforts with industry practices. It also simplifies compliance by mapping controls to regulatory requirements. Frameworks should be adapted to the organization’s environment. Combine them with internal policies and integrate them into development workflows, security reviews, and audit processes. ### 6. Conduct Red Teaming and Adversarial Testing AI systems must be tested against realistic attack scenarios. Red teaming simulates how adversaries might exploit models, including prompt injection, data extraction, or bypassing safeguards. This reveals weaknesses that standard testing may miss. Adversarial testing should include automated and manual techniques. Test inputs that attempt to manipulate outputs, access restricted data, or trigger unintended actions. Evaluate how the system behaves under stress and misuse. These exercises should be ongoing. As models and use cases evolve, new attack paths emerge. Regular testing helps validate defenses, improve controls, and ensure that AI systems remain resilient over time. ## AI Security with Venn Blue Border solves AI security for work taking place on any device. Using secure enclave technology that isolates and secures authorized work apps on any device, Blue Border gives IT the ability to govern AI tool access to company data – without deploying VDI or forcing employees to use company-managed devices. Work runs locally inside a company-controlled secure enclave on the user’s PC or Mac – without VDI or fully controlling the endpoint. Blue Border lets IT approve and deploy AI tools within the secure enclave – so the organization retains control over what those tools can see. Sanctioned AI tools run inside the secure enclave, allowing employees and contractors to use only company-sanctioned AI tools. Everything else stays outside the secure enclave and is invisible to company data. 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Both browser and installed apps run locally, natively, and securely. No hosting and no virtualization whatsoever. This approach preserves full app performance and user experience, while ensuring your organization’s DLP policies are always enforced. No file transfers, copy paste screenshots, or any other actions that could lead to data loss or compromise. Ready to see the future of remote work? Well, on behalf of all of us at Venn, we invite you to step inside the blue border. Find out more at Venn dot com.